GNN-Based Performance Prediction of Quantum Optimization of Maximum Independent Set
Atefeh Sohrabizadeh, Wan-Hsuan Lin, Daniel Bochen Tan, Madelyn Cain, Sheng-Tao Wang, Mikhail D. Lukin, Jason Cong · 2024
Maximum Independent Set (MIS) is an NP-hard optimization problem with wide-ranging applications in science and technology. Recently, a super-linear speedup over classical simulated annealing in solving MIS was experimentally observed using a Rydberg atom array (RAA) quantum computer. The extent of the observed speedup depended on the graph instance and the circuit depth of the quantum algorithm. Due to the limited availability of RAA, it is beneficial to be able to efficiently predict the quantum optimization performance on a given graph and circuit depth prior to running it. In this work, we present a graph neural network (GNN)-based performance predictor of the RAA-based MIS optimizer. Our experimental results achieve accuracy with an average root mean squared error (RMSE) of 0.03 out of the range [0, 1]. We open source the experimental data collected for this study at https://github.com/UCLA-VAST/RAAMIS.